Summary of A New Input Convex Neural Network with Application to Options Pricing, by Vincent Lemaire et al.
A new Input Convex Neural Network with application to options pricing
by Vincent Lemaire, Gilles Pagès, Christian Yeo
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel class of neural networks is introduced that leverages the principle of convex functions to approximate option prices with convex payoffs. The architecture of these networks is designed such that they are inherently convex with respect to their inputs, making them well-suited for approximating option prices. Convergence bounds are established to validate their approximation capabilities. A scrambling phase is also introduced to improve training. The effectiveness of these networks is demonstrated numerically in estimating prices for three types of options: Basket, Bermudan, and Swing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary These neural networks are designed to accurately estimate the prices of complex financial instruments like options with convex payoffs. They use a unique approach that makes them well-suited for this task. The network’s architecture is based on the idea that any convex function can be represented as the supremum of affine functions it dominates. This allows the network to capture complex relationships in option pricing models. |
